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| Shape-based BS algorithm for multiple change-points detection |
| ZHUANG Dan, LIU You-bo, MA Tie-feng |
1. School of Statistic, Southwestern University of Finance and Economics, Center of Statistical Research, Chengdu 611130, China;
2. School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China
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Abstract BS algorithm is one of the classical algorithms for multiple change-points detection,
it may bring about too many misjudgments and a high time complexity due to the procedure of global
CUSUM statistic. On one hand, the BS algorithm is an o?-line sequential method, therefore the data
timing information is not fully utilized. On the other hand, the principle of the BS algorithm to detect
the change-points is to maximize the CUSUM statistic, which does not consider the morphological
characteristics of the statistical constituent sequence. In view of these, the paper proposes an improved
BS algorithm, named Shape-based BS algorithm, which is based on local shape recognition. Basing
on the local pattern recognition of statistic sequence not only decreases the computational complexity,
but also avoids mutual interference among change-points, and it could also promote the robustness in
discerning change points. At last, this paper uses Shape-based BS algorithm to reduce the scenarios of
electric power, and achieves satisfactory practical results.
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Published: 05 July 2019
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多变点检测问题的Shape-based BS算法
BS算法是时间序列多变点检测中最经典的算法之一, 但是基于全
局CUSUM统计量的识别过程会带来过多误判和较高的时间复杂度. BS算法是一
种离线的序贯方法, 因此没有充分利用数据的时序信息; 另一方面, BS算法识别变点
的原则是CUSUM统计量最大化, 也没有考虑统计量构成序列的形态特性. 鉴于此, 提
出一种基于局部形态识别的BS改进算法, 命名为Shape-based BS算法. 基于局部形态
识别统计量, 不仅大大降低计算复杂度, 且降低了因变点间的互相干扰而带来的误判
率, 进而提升变点识别的稳健性. 最后, 将此算法应用到了电力系统的\场景压缩"问题
上, 具有满意的实用效果.
关键词：
多变点检测,
Shape-based BS算法,
形态识别,
场景压缩
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